val.py refactor (#4053)

* val.py refactor

* cleanup

* cleanup

* cleanup

* cleanup

* save after eval

* opt.imgsz bug fix

* wandb refactor

* dataloader to train_loader

* capitalize global variables

* runs/hub/exp to runs/detect/exp

* refactor wandb logging

* Refactor wandb operations (#4061)

Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>

models/common.py

@@ -1,5 +1,6 @@
 # YOLOv5 common modules
 
+import logging
 from copy import copy
 from pathlib import Path, PosixPath
 
@@ -15,7 +16,9 @@ from torch.cuda import amp
 from utils.datasets import exif_transpose, letterbox
 from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh, save_one_box
 from utils.plots import colors, plot_one_box
-from utils.torch_utils import time_synchronized
+from utils.torch_utils import time_sync
+
+LOGGER = logging.getLogger(__name__)
 
 
 def autopad(k, p=None):  # kernel, padding
@@ -226,7 +229,7 @@ class AutoShape(nn.Module):
         self.model = model.eval()
 
     def autoshape(self):
-        print('AutoShape already enabled, skipping... ')  # model already converted to model.autoshape()
+        LOGGER.info('AutoShape already enabled, skipping... ')  # model already converted to model.autoshape()
         return self
 
     @torch.no_grad()
@@ -240,7 +243,7 @@ class AutoShape(nn.Module):
         #   torch:           = torch.zeros(16,3,320,640)  # BCHW (scaled to size=640, 0-1 values)
         #   multiple:        = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...]  # list of images
 
-        t = [time_synchronized()]
+        t = [time_sync()]
         p = next(self.model.parameters())  # for device and type
         if isinstance(imgs, torch.Tensor):  # torch
             with amp.autocast(enabled=p.device.type != 'cpu'):
@@ -270,19 +273,19 @@ class AutoShape(nn.Module):
         x = np.stack(x, 0) if n > 1 else x[0][None]  # stack
         x = np.ascontiguousarray(x.transpose((0, 3, 1, 2)))  # BHWC to BCHW
         x = torch.from_numpy(x).to(p.device).type_as(p) / 255.  # uint8 to fp16/32
-        t.append(time_synchronized())
+        t.append(time_sync())
 
         with amp.autocast(enabled=p.device.type != 'cpu'):
             # Inference
             y = self.model(x, augment, profile)[0]  # forward
-            t.append(time_synchronized())
+            t.append(time_sync())
 
             # Post-process
             y = non_max_suppression(y, self.conf, iou_thres=self.iou, classes=self.classes, max_det=self.max_det)  # NMS
             for i in range(n):
                 scale_coords(shape1, y[i][:, :4], shape0[i])
 
-            t.append(time_synchronized())
+            t.append(time_sync())
             return Detections(imgs, y, files, t, self.names, x.shape)
 
 
@@ -323,31 +326,33 @@ class Detections:
 
             im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im  # from np
             if pprint:
-                print(str.rstrip(', '))
+                LOGGER.info(str.rstrip(', '))
             if show:
                 im.show(self.files[i])  # show
             if save:
                 f = self.files[i]
                 im.save(save_dir / f)  # save
-                print(f"{'Saved' * (i == 0)} {f}", end=',' if i < self.n - 1 else f' to {save_dir}\n')
+                if i == self.n - 1:
+                    LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to '{save_dir}'")
             if render:
                 self.imgs[i] = np.asarray(im)
 
     def print(self):
         self.display(pprint=True)  # print results
-        print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t)
+        LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' %
+                    self.t)
 
     def show(self):
         self.display(show=True)  # show results
 
-    def save(self, save_dir='runs/hub/exp'):
-        save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp', mkdir=True)  # increment save_dir
+    def save(self, save_dir='runs/detect/exp'):
+        save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True)  # increment save_dir
         self.display(save=True, save_dir=save_dir)  # save results
 
-    def crop(self, save_dir='runs/hub/exp'):
-        save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp', mkdir=True)  # increment save_dir
+    def crop(self, save_dir='runs/detect/exp'):
+        save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True)  # increment save_dir
         self.display(crop=True, save_dir=save_dir)  # crop results
-        print(f'Saved results to {save_dir}\n')
+        LOGGER.info(f'Saved results to {save_dir}\n')
 
     def render(self):
         self.display(render=True)  # render results